FIRE-pro

FIRE-pro identifies short linear protein motifs and characterizes their associations with quantitative proteomic phenotypes using an information-theoretic (mutual information) approach.

Key Features:

• Information-theoretic motif discovery: Uses mutual information to recover short, linear protein motifs from proteome-scale datasets.
• Data compatibility: Processes quantitative proteomic data types including sub-cellular localization, molecular function, protein half-life, and protein abundance.

Scientific Applications:

• Known motif recovery: Recovers established motifs such as phosphorylation sites and localization signals.
• Novel motif discovery: Identifies candidate sequence elements that do not match known motifs, indicating potential unexplored post-translational regulatory mechanisms.
• Linking motifs to systems-level behavior: Associates motifs with biological pathways and proteomic phenotypes to generate testable hypotheses about protein regulation.

Methodology:

Computes mutual information between sequence motifs and quantitative proteomic variables to detect motifs with preferential associations to biological pathways and non-random positioning within linear protein sequences.